Data Set Information:

Each patient could be in two possible categories CAD or Normal. A patient is categorized as CAD, if his/her diameter narrowing is greater than or equal to 50%, and otherwise as Normal .

Note 1: In this extension LAD, LCX, and RCA features have been added. CAD (Last column in dataset) happens when at least one of these three arteries is stenotic. To use this dataset only one of the LAD, LCX, RCA or Cath (Result of angiography) must be in dataset and the other ones must be eliminated for classification.

Note 2: This dataset not only can be used for CAD detection, but also for stenosis diagnosis of each LAD, LCX and RCA arteries.

Attribute Information:

The extension of Z-Alizadeh Sani dataset contains the records of 303 patients, each of which have 59 features.The features are arranged in four groups: demographic, symptom and examination, ECG, and laboratory and echo features.

Note 1: In this extension LAD, LCX, and RCA features have been added. CAD (Last column in dataset) happens when at least one of these three arteries is stenotic. To use this dataset only one of the LAD, LCX, RCA or Cath (Result of angiography) must be in dataset and the other ones must be eliminated for classification.

Note 2: This dataset not only can be used for CAD detection, but also for stenosis diagnosis of each LAD, LCX and RCA arteries.

Relevant Papers:

R. Alizadehsani, J. Habibi, M. J. Hosseini, H. Mashayekhi, R. Boghrati, A. Ghandeharioun, et al., 'A data mining approach for diagnosis of coronary artery disease,' Computer Methods and Programs in Biomedicine, vol. 111, pp. 52-61, 2013/07/01/ 2013.

R. Alizadehsani, J. Habibi, B. Bahadorian, H. Mashayekhi, A. Ghandeharioun, R. Boghrati, et al., 'Diagnosis of Coronary Arteries Stenosis Using Data Mining,' Journal of Medical Signals and Sensors, vol. 2, pp. 153-159, Jul-Sep

R. Alizadehsani, M. J. Hosseini, Z. A. Sani, A. Ghandeharioun, and R. Boghrati, 'Diagnosis of Coronary Artery Disease Using Cost-Sensitive Algorithms,' in 2012 IEEE 12th International Conference on Data Mining Workshops, 2012, pp. 9-16.

Z. Arabasadi, R. Alizadehsani, M. Roshanzamir, H. Moosaei, and A. A. Yarifard, 'Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm,' Computer Methods and Programs in Biomedicine, vol. 141, pp. 19-26, 2017/04/01/ 2017.

R. Alizadehsani, J. Habibi, Z. Alizadeh Sani, H. Mashayekhi, R. Boghrati, A. Ghandeharioun, et al., 'Diagnosing Coronary Artery Disease via Data Mining Algorithms by Considering Laboratory and Echocardiography Features,' Research in Cardiovascular Medicine, vol. 2, pp. 133-139, 07/31

R. Alizadehsani, J. Habibi, M. J. Hosseini, R. Boghrati, A. Ghandeharioun, B. Bahadorian, et al., 'Diagnosis of coronary artery disease using data mining techniques based on symptoms and ecg features,' European Journal of Scientific Research, vol. 82, pp. 542-553, 2012.

R. Alizadehsani, M. H. Zangooei, M. J. Hosseini, J. Habibi, A. Khosravi, M. Roshanzamir, et al., 'Coronary artery disease detection using computational intelligence methods,' Knowledge-based Systems, vol. 109, pp. 187-197, 2016/10/01/ 2016.

R. Alizadehsani, J. Habibi, Z. A. Sani, H. Mashayekhi, R. Boghrati, A. Ghandeharioun, et al., 'Diagnosis of Coronary Artery Disease Using Data mining based on Lab Data and Echo Features,' Journal of Medical and Bioengineering, vol. 1, 2012.

A. Roohallah, H. Mohammad Javad, B. Reihane, G. Asma, K. Fahime, and S. Zahra Alizadeh, 'Exerting Cost-Sensitive and Feature Creation Algorithms for Coronary Artery Disease Diagnosis,' International Journal of Knowledge Discovery in Bioinformatics (IJKDB), vol. 3, pp. 59-79, 2012.

R. Alizadehsani, M. J. Hosseini, Z. Alizadehsani, M. H. Mohammadi, O. Barati, and F. Khozeimeh, 'System for determining the need for Angiography in patients with symptoms of Coronary Artery disease,' ed: Google Patents, 2014.

F. Babi??, J. Olej??r, Z. Vantov??, and J. Parali??, 'Predictive and Descriptive Analysis for Heart Disease Diagnosis,' presented at the Federated Conference on Computer Science and Information Systems, 2017.

LOHITA, Kodali et al. Performance Analysis of Various Data Mining Techniques in the Prediction of Heart Disease. Indian Journal of Science and Technology, [S.l.], dec. 2015. ISSN 0974 -5645. Available at: <[Web link]>. Date accessed: 17 Nov. 2017. [Web link].

J. Bekta??, T. Ibrik?§i, and I. ?–zcan, 'Classification of Real Imbalanced Cardiovascular Data Using Feature Selection and Sampling Methods: A Case Study with Neural Networks and Logistic Regression,' International Journal on Artificial Intelligence Tools, 2017.

C. Yadav, S. Lade, and M. K. Suman, 'Predictive Analysis for the Diagnosis of Coronary Artery Disease using Association Rule Mining,' International Journal of Computer Applications, vol. 87, 2014.

Citation Request:

Extension of Z-Alizadeh Sani Dataset User Agreement

I agree with following items.
a€￠ To cite  Z-Alizadeh Sani Dataset in any paper of mine or my collaborators that makes any use of the database. The reference is:

1. R. Alizadehsani et al., a€?A data mining approach for diagnosis of coronary artery disease,a€? Computer Methods and Programs in Biomedicine, vol.111, no.1, pp.52-61, Jul. 2013.
2. R. Alizadehsani, M.H. Zangooei, M.J. Hosseini, J. Habibi, A. Khosravi, M. Roshanzamir, F. Khozeimeh, N. Sarrafzadegan, S. Nahavandi, Coronary artery disease detection using computational intelligence methods, Knowledge-based Systems, 109 (2016) 187-197

a€￠ To use the dataset for research purposes only.

Provide the names, email addresses, institutions, and other contact information of the donors and creators of the data set.

Name:Dr Zahra Alizadeh Sani,Associate Professor of cardiology,
email:Drzas '@' rhc.ac.ir,
institution: Cardiovascular Imaging Department, Rajaei Cardiovascular, Medical & Research Center, Iran University , Tehran, Iran.
Post code:1996911151
website: http://dralizadehsani.rhc.ac.ir/Files/Forms/2016-11-13_01.46.29_dr.alizadeh.CV.pdf

Name:Roohallah Alizadehsani, PhD student
email: alizadeh_roohallah '@' yahoo.com
institution: Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria 3217, Australia.
website: http://ce.sharif.ir/~ralizadeh/

Name: Mohamad Roshanzamir, PhD candidate
email: mohamad.roshanzamir '@' ec.iut.ac.ir
institution: Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran.